Resuscitation
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The primary aim of this systematic review was to investigate the most common electroencephalogram (EEG)-based machine learning (ML) model with the highest Area Under Receiver Operating Characteristic Curve (AUC) in two ML categories, conventional ML and Deep Neural Network (DNN), to predict the neurologic outcomes after cardiac arrest; the secondary aim was to investigate common EEG features applied to ML models. ⋯ RF and CNN were the two most common ML models with the highest AUCs for predicting the neurologic outcomes after cardiac arrest. Using a multimodal model that combines EEG features and electronic health record data may further improve prognostic performance.
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Observational Study
Trends in community response and long term outcomes from paediatric cardiac arrest: A retrospective observational study.
This study aimed to investigate trends over time in pre-hospital factors for pediatric out-of-hospital cardiac arrest (pOHCA) and long-term neurological and neuropsychological outcomes. These have not been described before in large populations. ⋯ Long-term favorable neurological outcome, assessed at a median 2.5 years follow-up, improved significantly over the study period. Total IQ scores did not significantly change over time. Furthermore, AED use (OR 1.21, 95%CI 1.10-1.33) and shockable rhythms among adolescents (OR1.15, 95%CI 1.02-1.29) increased over time.
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Many rapid response system (RRS) events are activated using multiple triggers. However, the patterns in which multiple RRS triggers occur together to activate RRS events are unknown. The purpose of this study was to identify these patterns (RRS trigger clusters) and determine their association with outcomes among hospitalized adult patients. ⋯ We discovered six novel RRS trigger clusters with differing relationships to adverse patient outcomes. RRS trigger clusters may prove crucial in clarifying the associations between RRS events and adverse outcomes and aiding in clinician decision-making during RRS events.